Biotic interactions are crucial for determining the structure and dynamics of communities; however, direct measurement of these interactions can be challenging in terms of time and resources, especially when numerous species are involved. Inferring species interactions from species co-occurrence patterns is increasingly being used; however, recent studies have highlighted some limitations. To our knowledge, no attempt has been made to test the accuracy of the existing methods for detecting mutualistic interactions in terrestrial ecosystems. In this study, we compiled two literature-based, long-term datasets of interactions between butterflies and herbaceous plant species in two regions of Germany and compared them with observational abundance and presence/absence data collected within a year in the same regions. We tested how well the species associations generated by three different co-occurrence analysis methods matched those of empirically measured mutualistic associations using sensitivity and specificity analyses and compared the strength of associations. We also checked whether flower abundance data (instead of plant abundance data) increased the accuracy of the co-occurrence models and validated our results using empirical flower visitation data. The results revealed that, although all methods exhibited low sensitivity, our implementation of the Relative Interaction Intensity index with pairwise null models performed the best, followed by the probabilistic method and Spearman's rank correlation method. However, empirical data showed a significant number of interactions that were not detected using co-occurrence methods. Incorporating flower abundance data did not improve sensitivity but enhanced specificity in one region. Further analysis demonstrated incongruence between the predicted co-occurrence associations and actual interaction strengths, with many pairs exhibiting high interaction strength but low co-occurrence or vice versa. These findings underscore the complexity of ecological dynamics and highlight the limitations of current co-occurrence methods for accurately capturing species interactions.
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http://dx.doi.org/10.1002/ece3.70498 | DOI Listing |
Alzheimers Dement
December 2024
Allen Institute for Brain Science, Seattle, WA, USA.
Background: Applying single-cell RNA sequencing (scRNA-seq) to the study of neurodegenerative disease has propelled the field towards a more refined cellular understanding of Alzheimer's disease (AD); however, directly linking protein pathology to transcriptomic changes has not been possible at scale. Recently, a high-throughput method was developed to generate high-quality scRNA-seq data while retaining cytoplasmic proteins. Tau is a cytoplasmic protein and when hyperphosphorylated is integrally involved in AD progression.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Background: Aging is a time-dependent deterioration of physiological functions that occurs in both humans and animals. Within the brain, aging cells gradually become dysfunctional through a complex interplay of intrinsic and extrinsic factors, ultimately leading to behavioral deficits and enhanced risk of neurodegenerative diseases such as Alzheimer's disease (AD). The characteristics of normal aging are distinct from those associated with age-related diseases and it is important to understand the processes that contribute to this pathological divergence.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: In Alzheimer's disease (AD), specific brain regions become vulnerable to pathology while others remain resilient. New methods of imaging such as highly multiplexed immunofluorescence (MxIF) provide an abundance of spatial information, while analytical techniques like machine learning (ML) can address questions of cellular contributors to this regional vulnerability.
Method: We performed MxIF staining for 26 markers and compared postmortem human samples from an AD-susceptible brain area, the prefrontal cortex (PFC, Brodmann's areas 9, 10 or 46) to an AD-resilient brain area, the primary visual cortex (V1, area 17).
Background: Neuropathologic inclusions formed by hyperphosphorylated protein tau in the brain are a hallmark of Alzheimer's disease and other human neurodegenerative disorders commonly referred to as tauopathies. Tau lesions differ in their disease-specific morphological presentations, affected cell type, subcellular compartments and tau isoforms present in the inclusions. In addition, tau filaments isolated from different tauopathies have distinct fibrillar structures that potentially underlie the morphological diversity of tau lesions.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA.
Background: While immune function is known to play a mechanistic role in Alzheimer's disease (AD), whether immune proteins in peripheral circulation influence the rate of amyloid-b (Aβ) progression remains unknown.
Method: Using the Baltimore Longitudinal Study of Aging (BLSA; n = 196; mean follow-up: 5 years/4 scans), we identified immune-related proteins in plasma (candidate proteins) related to rates of change in cortical Aβ levels, as measured by C-PiB PET. Along with identifying genetic variants that contributed to candidate protein associations, characterizing their relationships with tau-PET and changes in ADRD biomarkers (Aβ, NfL, GFAP, pTau-181), and assessing their expression patterns in human microglia, we leveraged data from the Atherosclerosis Risk in Communities (ARIC) study to determine if changes in candidate protein levels precede Ab = β onset (n = 272), and whether they predict 20-year dementia risk during mid-life (n = 11,596) and 8-year dementia risk during late-life (n = 4,288).
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